Library resources are critical in supporting teaching, research and learning processes. Several universities have employed online platforms and infrastructure for enabling the online services to students, faculty and staff. To provide efficient services by understanding and predicting user needs libraries are looking into the area of data analytics. Library analytics in Singapore Management University is the project committed to provide an interface for data-intensive project collaboration, while supporting one of the library’s key pillars on its commitment to collaborate on initiatives with SMU Communities and external groups. In this paper, we study the transaction logs for user behavior analysis that can aid library admin to make operational decisions. The main challenges include the data quality and enormous datasets. Our solution not only provides the approach to data cleaning process but also suggest better visualization techniques for the user dashboard. Our experiment shows that the data cleaning process was effective in producing the insights from the library usage and the visualization techniques are efficient to summarize the big data. We used the datasets from Singapore Management University for this project.